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Towards the development of a 30 year record of remotely sensed vegetation optical depth

机译：建立30年的遥感植被光学深度记录

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摘要

The framework for the development of a 30 year global record of remotely sensed vegetation optical depth is presented. The vegetation data set is derived from passive microwave observations and spans the period from November 1978 through the end of 2008. Different satellite sensor observations (i.e. Nimbus-7 SMMR, DMSP SSM/I, TRMM TMI, and AQUA AMSR-E). are used in a radiative transfer model to derive vegetation optical depth. Vegetation optical depth can directly be related to vegetation water content and is a function of biomass. The retrieval model is described and the quality of the retrieved vegetation optical depth is discussed. The new dataset will be merged into one consistent global product for the entire period of data record. To explore the potential to use this new product for long term vegetation modeling, the product was compared to total biomass from the biogeochemical model CASA. The results indicate that the vegetation optical depth can be an important contribution to the derivation of biophysical properties like biomass. It can also increase the reliability of optical sensor derived vegetation indices, because the microwave vegetation optical depth can be derived under cloudy conditions. This unique feature could create the possibility to improve the temporal resolution of other biophysical data products. The entire vegetation density dataset will be made available for download by the general science community and could give a significant contribution in climate research.
机译：提出了发展全球30年遥感植被光学深度记录的框架。植被数据集来自无源微波观测，范围从1978年11月到2008年底。不同的卫星传感器观测值（即Nimbus-7 SMMR，DMSP SSM / I，TRMM TMI和AQUA AMSR-E）。在辐射传递模型中使用来推导植被的光学深度。植被的光学深度可以直接与植被含水量有关，并且是生物量的函数。描述了检索模型，并讨论了检索到的植被光学深度的质量。在整个数据记录期间，新的数据集将合并为一个一致的全局产品。为了探索将这种新产品用于长期植被建模的潜力，将该产品与来自生物地球化学模型CASA的总生物量进行了比较。结果表明，植被的光学深度可能对生物物理特性（如生物量）的推导起重要作用。由于微波植被的光学深度可以在多云的条件下得出，因此它也可以提高光学传感器得出的植被指数的可靠性。此独特功能可能会提高其他生物物理数据产品的时间分辨率。整个植被密度数据集将可供普通科学界下载，并可为气候研究做出重大贡献。

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